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C. Midoglu, A. Storås, S. S. Sabet, M. Hammou, S. Hicks, I. Strümke, M. Riegler, C. Griwodz and P. Halvorsen

Experiences and Lessons Learned from a Crowdsourced-Remote Hybrid User Survey Framework

In 2022 IEEE International Symposium on Multimedia (ISM). Italy: IEEE, 2022.
Proceedings, refereed
2022
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J. Amann, D. Vetter, S. N. Blomberg, H. C. Christensen, M. Coffee, S. Gerke, T. K. Gilbert, T. Hagendorff, S. Holm, M. Livne et al.

To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems

PLOS Digital Health 1, no. 2 (2022): e0000016.
Journal Article
2022
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A. Storås, A. Åsberg, P. Halvorsen, M. Riegler and I. Strümke

Predicting Tacrolimus Exposure in Kidney Transplanted Patients Using Machine Learning

In 35th IEEE CBMS International Symposium on Computer-Based Medical Systems. IEEE, 2022.
Proceedings, refereed
2022
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A. Storås, R. Prabhu, H. L. Hammer and I. Strümke

Bias og kvantitativ analyse innen velferd

Tidsskrift for velferdsforskning 25, no. 3 (2022): 1-24.
Journal Article
2022
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News

19 April 2021

New tool and guide on evaluation metrics for medical applications of AI

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2022

Public outreach

O. Lysne, I. Strümke and M. Riegler

Data samlet fra åpne kilder er ikke ufarlig

Kronikk: Aftenposten, 2022.
Status: Published

Miscellaneous

S. Hicks, A. Storås, M. Riegler, C. Midoglu, M. Hammou, T. de Lange, S. Parasa, P. Halvorsen and I. Strümke

Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanations

arXiv, 2022.
Status: Published
2021

Journal articles

D. V. Fryer, I. Strümke and H. Nguyen

Model independent feature attributions: Shapley values that uncover non-linear dependencies

PeerJ Computer Science 7 (2021): e582.
Status: Published
D. Fryer, I. Strümke and H. Nguyen

Shapley Values for Feature Selection: The Good, the Bad, and the Axioms

IEEE Access 9 (2021): 144352-144360.
Status: Published
S. Hicks, J. L. Isaksen, V. Thambawita, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, I. Strümke, C. Ellervik, M. S. Olesen et al.

Explaining deep neural networks for knowledge discovery in electrocardiogram analysis

Scientific Reports 11 (2021): 10949.
Status: Published
I. Strümke, M. Slavkovik and V. I. Madai

The social dilemma in artificial intelligence development and why we have to solve it

AI and Ethics (2021).
Status: Published
V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen et al.

DeepFake electrocardiograms: the key for open science for artificial intelligence in medicine

Scientific Reports (2021).
Status: Accepted
V. Thambawita, J. L. Isaksen, S. Hicks, J. Ghouse, G. Ahlberg, A. Linneberg, N. Grarup, C. Ellervik, M. S. Olesen, T. Hansen et al.

DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine

Nature Scientific Reports 11 (2021): 21896.
Status: Published
V. Thambawita, I. Strümke, S. Hicks, P. Halvorsen, S. Parasa and M. Riegler

Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images

Diagnostics 11, no. 12 (2021).
Status: Published

Proceedings, refereed

V. Thambawita, I. Strümke, S. Hicks, M. Riegler, P. Halvorsen and S. Parasa

Data Augmentation Using Generative Adversarial Networks For Creating Realistic Artificial Colon Polyp Images

In DDW 2021, 2021.
Status: Published